Characterizing lung nodules on computed tomography (CT) images is a challenging clinical task. Granulomas, which are benign presentations but appear as malignant nodules on chest CT scans, are considered among the most difficult tumor confounders to discern. Adenocarcinomas are malignant lesions or nodules. Granulomas and adenocarcinomas are often indistinguishable on both CT and positron emission tomography (PET) scans. For example, both types of nodules appear “hot” on PET imagery. Hence, many people with benign nodules are subjected to unnecessary surgical procedures due to the inability to make confident diagnostic predictions with respect to the nodule on CT. Consequently there is a need for discriminating radiomic features for improved characterization of lung nodules on CT scans.
Existing approaches for distinguishing adenocarcinoma from granuloma employ textural, intensity, or shape analysis for radiomic characterization of lung nodules. For example, shape features (e.g., surface area, volume, and surface to volume ratio), together with textural and intensity features extracted from CT data of lung and oropharyngeal cancers, may be associated, using unsupervised clustering, with underlying gene-expression profiles of lung cancer patients. Existing approaches may employ automated three dimensional (3D) active contour segmentation to segment nodules and then extract morphological and textural features from the segmented nodules. One existing approach analyzed with a leave-one out cross validation yielded an AUC of 0.83 in a data set of 44 malignant and 52 benign nodules. Another existing approach achieved AUC values between 0.68 and 0.92 with 48 malignant and 33 benign nodules. One common attribute associated with the majority of these existing radiomic related approaches for lung nodule characterization is that they involve features pertaining to the nodule alone. Furthermore, these existing texture-based features tend to be affected by the choice of scanner, reconstruction kernel, and slice thickness.
Lymphocytic infiltration is associated with malignant lung nodules. The infiltration appears within the perinodular space of malignant nodules, which in turn causes differential textural patterns adjacent to the nodule. However, lymphocytic infiltration does not typically co-occur with granulomas and benign nodules. Some attempts have been proposed towards this end through the concept of margin sharpness. One existing version of the margin sharpness descriptor approach calculates the sharpness of the intensity transition across the lesion. However, existing margin sharpness approaches limit the interrogation of intensity changes to the nodule interface. Existing approaches ignore the whole core of the tumor. Thus, existing approaches for distinguishing adenocarcinoma from granuloma are sub-optimal.